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This paper proposes a scheme of detecting the screening data of hematopoietic tumor patients, using block-matching-based self-organizing maps. The data of an examinee frequently lacks several of the item values, and hence the data is presented to a map after averages of non-missing item values are substituted for items with no values. It is basically judged, by observing the label of a winner block...
Data detection using self organizing maps is presented for hematopoietic tumor patients. The learning data for the maps is generated from the screening data. Redundant items, which have an unfavorable influence on data detection and are common to all the data, are eliminated by a genetic algorithm and an immune algorithm. It is basically judged, by observing a label of a winner neuron in a map, whether...
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